Spain is the third-largest producer of pork meat in the world, and many farms
in several regions depend on the evolution of this market. However, the current
pricing system is unfair, as some actors have better market information than
others. In this context, historical pricing is an easy-to-find and affordable
data source that can help all agents to be better informed. However, the time
lag in data acquisition can affect their pricing decisions. In this paper, we
study the effect that data acquisition delay has on a price prediction system
using multiple prediction algorithms. We describe the integration of the best
proposal into a decision support system prototype and test it in a real-case
scenario. Specifically, we use public data from the most important regional
pork meat markets in Spain published by the Ministry of Agriculture with a
two-week delay and subscription-based data of the same markets obtained on the
same day. The results show that the error difference between the best public
and data subscription models is 0.6 Euro cents in favor of the data without
delay. The market dimension makes these differences significant in the supply
chain, giving pricing agents a better tool to negotiate market prices.

本论文研究数据采集延迟对价格预测系统的影响，使用多种预测算法进行了测试，并将其最佳提议整合到决策支持系统原型中，在实际案例中进行了测试。结果表明，最佳公共和数据订阅模型之间的误差差异为 0.6 欧元分，支持数据没有延迟的。